Empirical forecasting practices of a British university

This article is based on a single case study aimed at examining behavioral issues of forecasting, in particular the role and practice of forecasting in a British university settings. Key variables were identified in establishing associations between the variables that provide suitable criteria for the purpose of this study. Data collection was based on questionnaires distributed to people involved and interviews which were held with prominent staff of the University. Fisher-exact tests were performed to identify significant associations between variables. Results indicated the various levels of perceptions and practices of forecasting produced by the people involved at the University. The study implies that useful insights can be gathered through forecasting from a different perspective of the non-profit making service industry

Forecasting Player Behavioral Data and Simulating in-Game Events

Understanding player behavior is fundamental in game data science. Video games evolve as players interact with the game, so being able to foresee player experience would help to ensure a successful game development. In particular, game developers need to evaluate beforehand the impact of in-game events. Simulation optimization of these events is crucial to increase player engagement and maximize monetization. We present an experimental analysis of several methods to forecast game-related variables, with two main aims: to obtain accurate predictions of in-app purchases and playtime in an operational production environment, and to perform simulations of in-game events in order to maximize sales and playtime. Our ultimate purpose is to take a step towards the data-driven development of games. The results suggest that, even though the performance of traditional approaches such as ARIMA is still better, the outcomes of state-of-the-art techniques like deep learning are promising. Deep learning comes up as a well-suited general model that could be used to forecast a variety of time series with different dynamic behaviors

Asset Prices, Traders' Behavior, and Market Design

The dynamics in a financial market with heterogeneous agents is analyzed under different market architectures. We start with a tractable behavioral model under Walrasian market clearing and simulate it under more realistic trading protocols. The key behavioral feature of the model is the switching of agents between simple forecasting rules on the basis of fitness measure. Analyzing the dynamics under order-driven protocols we show that behavioral and structural assumptions of the model are closely intertwined. High responsiveness of agents to a fitness measure causes excess volatility, however the frictions of the order-driven markets may stabilize the dynamics.

Forecasting aggregate stock returns using the number of initial public offerings as a predictor

Large number of Initial Public Offerings (IPOs) reliably predicts subsequent low equally weighted aggregate stock returns and the return differential between small and big firms, both in-sample and out-of-sample. The forecasting patterns are consistent with a behavioral story featuring investor sentiment and limits to arbitrage.Initial Public Offerings

A simultaneous equations analysis of analysts’ forecast bias and institutional ownership

In this paper we use a simultaneous equations model to examine the relationship between analysts' forecasting decisions and institutions' investment decisions. Neglecting their interaction results in model misspecification. We find that analysts' optimism concerning a firm's earnings responds positively to changes in the number of institutions holding the firm's stock. At the same time, institutional demand responds positively to increases in analysts' optimism. We also investigate several firm characteristics as determinants of analysts' and institutions' decisions. We conclude that agency-driven behavioral considerations are significant.Financial institutions ; Forecasting ; Financial markets

Asset Return Dynamics and Learning

This paper advocates a theory of expectation formation that incorporates many of the central motivations of behavioral finance theory while retaining much of the discipline of the rational expectations approach. We provide a framework in which agents, in an asset pricing model, underparameterize their forecasting model in a spirit similar to Hong, Stein, and Yu (2005) and Barberis, Shleifer, and Vishny (1998), except that the parameters of the forecasting model, and the choice of predictor, are determined jointly in equilibrium. We show that multiple equilibria can exist even if agents choose only models that maximize (risk-adjusted) expected profits. A real-time learning formulation yields endogenous switching between equilibria. We demonstrate that a real-time learning version of the model, calibrated to U.S. stock data, is capable of reproducing many of the salient empirical regularities in excess return dynamics such as under/overreaction, persistence, and volatility clustering.Asset pricing, misspecification, behavioral finance, predictability, adaptive learning

Financial markets forecasts revisited: are they rational, herding or bold?

We test whether professional forecasters forecast rationally or behaviorally using a unique database, QSS Database, which is the monthly panel of forecasts on Japanese stock prices and bond yields. The estimation results show that (i) professional forecasts are behavioral, namely, significantly influenced by past forecasts, (ii) there exists a stock-bond dissonance: while forecasting behavior in the stock market seems to be herding, that in the bond market seems to be bold in the sense that their current forecasts tend to be negatively related to past forecasts, and (iii) the dissonance is due, at least partially, to the individual forecasters' behavior that is influenced by their own past forecasts rather than others. Even in the same country, forecasting behavior is quite different by market.

Learning under misspecification: a behavioral explanation of excess volatility in stock prices and persistence in inflation

We propose a simple misspecification equilibrium concept and a behavioral learning process explaining excess volatility in stock prices and high persistence in inflation. Boundedly rational agents use a simple univariate linear forecasting rule and in equilibrium correctly forecast the unconditional sample mean and first-order sample autocorrelation. In the long run, agents thus learn the best univariate linear forecasting rule, without fully recognizing the structure of the economy. In a first application, an asset pricing model with AR(1) dividends, a unique stochastic consistent expectations equilibrium (SCEE) exists characterized by high persistence and excess volatility, and it is globally stable under learning. In a second application, the New Keynesian Phillips curve, multiple SCEE arise and a low and a high persistence misspecification equilibrium co-exist. Learning exhibits path dependence and inflation may switch between low and high persistence regimes.

Microsimulations in the Presence of Heterogeneity

This paper develops a method that improves researchers’ ability to account for behavioral responses to policy change in microsimulation models. Current microsimulation models are relatively simple, in part because of the technical difficulty of accounting for unobserved heterogeneity. This is all the more problematic because data constraints typically force researchers to limit their forecasting models to relatively few, mostly time-invariant explanatory covariates, so that much of the variation across individuals is unobserved. Furthermore, failure to account for unobservables often leads to biased estimates of structural parameters, which are critically important for measuring behavioral responses. This paper develops a theoretical approach to incorporate (univariate and multivariate) unobserved heterogeneity into microsimulation models; illustrates computer algorithms to efficiently implement heterogeneity in continuous and limited dependent models; and evaluates the importance of unobserved heterogeneity by conducting Monte Carlo simulations.